OpenRaiser/Intern-Atlas huggingface dataset:
Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE
Venue: arXiv cs.CL 2025
Year: 2025

Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where multiple candidates are generated at each step. We identify a key limitation in this approach: the candidates at the same step are derived from the same representation, limiting diversity and reducing overall effectiveness. To address this, we propose Jakiro, leveraging Mixture of Experts (MoE), where independent experts generate diverse predictions, effectively decoupling correlations among candidates. Furthermore, we introduce a hybrid inference strategy, combining autoregressive decoding for initial tokens with parallel decoding for subsequent stages, and enhance the latter with contrastive mechanism in features to improve accuracy. Our method significantly boosts prediction accuracy and achieves higher inference speedups. Extensive experiments across diverse models validate the effectiveness and robustness of our approach, establishing a new SOTA in speculative decoding. Our codes are available at https://github.com/haiduo/Jakiro.
Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously Optimizing Robot Poses and Occupancy Map
Venue: arXiv cs.RO 2025
Year: 2025

Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations because they effectively classify spaces into obstacles, free areas, and unknown regions, providing robots with spatial information for various tasks. In this paper, we propose Occupancy-SLAM, a novel optimization-based SLAM method that enables the joint optimization of robot trajectory and the occupancy map through a parameterized map representation. The key novelty lies in optimizing both robot poses and occupancy values at different cell vertices simultaneously, a significant departure from existing methods where the robot poses need to be optimized first before the map can be estimated. Evaluations using simulations and practical 2D laser datasets demonstrate that the proposed approach can robustly obtain more accurate robot trajectories and occupancy maps than state-of-the-art techniques with comparable computational time. Preliminary results in the 3D case further confirm the potential of the proposed method in practical 3D applications, achieving more accurate results than existing methods.
RustMC: Extending the GenMC stateless model checker to Rust
Venue: arXiv cs.PL 2025
Year: 2025

RustMC is a stateless model checker that enables verification of concurrent Rust programs. As both Rust and C/C++ compile to LLVM IR, RustMC builds on GenMC which provides a verification framework for LLVM IR. This enables the automatic verification of Rust code and any C/C++ dependencies. This tool paper presents the key challenges we addressed to extend GenMC. These challenges arise from Rust's unique compilation strategy and include intercepting threading operations, handling memory intrinsics and uninitialized accesses. Through case studies adapted from real-world programs, we demonstrate RustMC's effectiveness at finding concurrency bugs stemming from unsafe Rust code, FFI calls to C/C++, and incorrect use of atomic operations.
Data-aware Dynamic Execution of Irregular Workloads on Heterogeneous Systems
Venue: arXiv cs.DC 2025
Year: 2025

Current approaches to scheduling workloads on heterogeneous systems with specialized accelerators often rely on manual partitioning, offloading tasks with specific compute patterns to accelerators. This method requires extensive experimentation and human effort to identify the tasks suitable for the accelerator. To solve this problem, we introduce DyPe, a scheduling framework tailored for heterogeneous systems with specialized accelerators. Our method automatically partitions, deploys, and reschedules execution when necessary by dynamically analyzing the characteristics of the input data and leveraging the interoperator parallelism among heterogeneous devices. DyPe navigates a multi-objective, multi-constraint design space that considers both system constraints and application requirements, which allows it to discover Pareto-optimal mapping configurations, improving the system's overall performance and effectively managing energy-performance trade-offs. To demonstrate the benefits of our approach on real hardware, we build a heterogeneous system of GPUs and FPGAs with peer-to-peer data transfers. The experiments show that conventional static scheduling is optimal for 13 out of 86 cases for different workloads and system settings while DyPe is adaptable and able to find the optimal schedule in 77 out of 86 cases, with an average of only 3.95% performance or energy efficiency loss in the sub-optimal cases. Performance evaluation of DyPe shows an average of 1.53x throughput and 1.09x energy efficiency improvement over the static schedule baseline and 1.44x throughput and 1.66x energy efficiency over the GPU-only baseline.
The digital labour of artificial intelligence in Latin America: a comparison of Argentina, Brazil, and Venezuela
Venue: arXiv cs.CY 2025
Year: 2025

The current hype around artificial intelligence (AI) conceals the substantial human intervention underlying its development. This article lifts the veil on the precarious and low-paid 'data workers' who prepare data to train, test, check, and otherwise support models in the shadow of globalized AI production. We use original questionnaire and interview data collected from 220 workers in Argentina (2021-22), 477 in Brazil (2023), and 214 in Venezuela (2021-22). We compare them to detect common patterns and reveal the specificities of data work in Latin America, while disclosing its role in AI production.We show that data work is intertwined with economic hardship, inequalities, and informality. Despite workers' high educational attainment, disadvantage is widespread, though with cross-country disparities. By acknowledging the interconnections between AI development, data work, and globalized production, we provide insights for the regulation of AI and the future of work, aiming to achieve positive outcomes for all stakeholders.
Accelerating Outlier-robust Rotation Estimation by Stereographic Projection
Venue: arXiv cs.CV 2025
Year: 2025

Rotation estimation plays a fundamental role in many computer vision and robot tasks. However, efficiently estimating rotation in large inputs containing numerous outliers (i.e., mismatches) and noise is a recognized challenge. Many robust rotation estimation methods have been designed to address this challenge. Unfortunately, existing methods are often inapplicable due to their long computation time and the risk of local optima. In this paper, we propose an efficient and robust rotation estimation method. Specifically, our method first investigates geometric constraints involving only the rotation axis. Then, it uses stereographic projection and spatial voting techniques to identify the rotation axis and angle. Furthermore, our method efficiently obtains the optimal rotation estimation and can estimate multiple rotations simultaneously. To verify the feasibility of our method, we conduct comparative experiments using both synthetic and real-world data. The results show that, with GPU assistance, our method can solve large-scale ($10^6$ points) and severely corrupted (90\% outlier rate) rotation estimation problems within 0.07 seconds, with an angular error of only 0.01 degrees, which is superior to existing methods in terms of accuracy and efficiency.
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
Venue: arXiv cs.DC 2025
Year: 2025

Multimodal transformers integrate diverse data types like images, audio, and text, advancing tasks such as audio-visual understanding and image-text retrieval; yet their high parameterization limits deployment on resource-constrained edge devices. Split Learning (SL), which partitions models at a designated cut-layer to offload compute-intensive operations to the server, offers a promising approach for distributed training of multimodal transformers, though its application remains underexplored. We present MPSL, a parallel SL approach for computational efficient fine-tuning of multimodal transformers in a distributed manner, while eliminating label sharing, client synchronization, and per-client sub-model management. MPSL employs lightweight client-side tokenizers and a unified modality-agnostic encoder, allowing flexible adaptation to task-specific needs. Our evaluation across 7 multimodal datasets demonstrates that MPSL matches or outperforms Federated Learning, reduces client-side computations by 250x, and achieves superior scalability in communication cost with model growth. Through extensive analysis, we highlight task suitability, trade-offs, and scenarios where MPSL excels, inspiring further exploration.
Weld n'Cut: Automated fabrication of inflatable fabric actuators
Venue: arXiv cs.RO 2025
Year: 2025

Lightweight, durable textile-based inflatable soft actuators are widely used in soft robotics, particularly for wearable robots in rehabilitation and in enhancing human performance in demanding jobs. Fabricating these actuators typically involves multiple steps: heat-sealable fabrics are fused with a heat press, and non-stick masking layers define internal chambers. These layers must be carefully removed post-fabrication, often making the process labor-intensive and prone to errors. To address these challenges and improve the accuracy and performance of inflatable actuators, we introduce the Weld n'Cut platform-an open-source, automated manufacturing process that combines ultrasonic welding for fusing textile layers with an oscillating knife for precise cuts, enabling the creation of complex inflatable structures. We demonstrate the machine's performance across various materials and designs with arbitrarily complex geometries.
Structure-preserving contrastive learning for spatial time series
Venue: arXiv cs.LG 2025
Year: 2025

The effectiveness of neural network models largely relies on learning meaningful latent patterns from data, where self-supervised learning of informative representations can enhance model performance and generalisability. However, self-supervised representation learning for spatially characterised time series, which are ubiquitous in transportation domain, poses unique challenges due to the necessity of maintaining fine-grained spatio-temporal similarities in the latent space. In this study, we introduce two structure-preserving regularisers for the contrastive learning of spatial time series: one regulariser preserves the topology of similarities between instances, and the other preserves the graph geometry of similarities across spatial and temporal dimensions. To balance the contrastive learning objective and the need for structure preservation, we propose a dynamic weighting mechanism that adaptively manages this trade-off and stabilises training. We validate the proposed method through extensive experiments, including multivariate time series classification to demonstrate its general applicability, as well as macroscopic and microscopic traffic prediction to highlight its particular usefulness in encoding traffic interactions. Across all tasks, our method preserves the similarity structures more effectively and improves state-of-the-art task performances. This method can be integrated with an arbitrary neural network model and is particularly beneficial for time series data with spatial or geographical features. Furthermore, our findings suggest that well-preserved similarity structures in the latent space indicate more informative and useful representations. This provides insights to design more effective neural networks for data-driven transportation research. Our code is made openly accessible with all resulting data at https://github.com/yiru-jiao/spclt
Generating Privacy-Preserving Personalized Advice with Zero-Knowledge Proofs and LLMs
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To address these challenges, we propose a framework that integrates zero-knowledge proof (ZKP) technology, specifically zkVM, with LLM-based chatbots. This integration enables privacy-preserving data sharing by verifying user traits without disclosing sensitive information. Our research introduces both an architecture and a prompting strategy for this approach. Through empirical evaluation, we clarify the current constraints and performance limitations of both zkVM and the proposed prompting strategy, thereby demonstrating their practical feasibility in real-world scenarios.
Rethinking Large-scale Dataset Compression: Shifting Focus From Labels to Images
Venue: arXiv cs.CV 2025
Year: 2025

Dataset distillation and dataset pruning are two prominent techniques for compressing datasets to improve computational and storage efficiency. Despite their overlapping objectives, these approaches are rarely compared directly. Even within each field, the evaluation protocols are inconsistent across various methods, which complicates fair comparisons and hinders reproducibility. Considering these limitations, we introduce in this paper a benchmark that equitably evaluates methodologies across both distillation and pruning literatures. Notably, our benchmark reveals that in the mainstream dataset distillation setting for large-scale datasets, which heavily rely on soft labels from pre-trained models, even randomly selected subsets can achieve surprisingly competitive performance. This finding suggests that an overemphasis on soft labels may be diverting attention from the intrinsic value of the image data, while also imposing additional burdens in terms of generation, storage, and application. To address these issues, we propose a new framework for dataset compression, termed Prune, Combine, and Augment (PCA), which focuses on leveraging image data exclusively, relies solely on hard labels for evaluation, and achieves state-of-the-art performance in this setup. By shifting the emphasis back to the images, our benchmark and PCA framework pave the way for more balanced and accessible techniques in dataset compression research. Our code is available at: https://github.com/ArmandXiao/Rethinking-Dataset-Compression
SparseFocus: Learning-based One-shot Autofocus for Microscopy with Sparse Content
Venue: arXiv cs.CV 2025
Year: 2025

Autofocus is necessary for high-throughput and real-time scanning in microscopic imaging. Traditional methods rely on complex hardware or iterative hill-climbing algorithms. Recent learning-based approaches have demonstrated remarkable efficacy in a one-shot setting, avoiding hardware modifications or iterative mechanical lens adjustments. However, in this paper, we highlight a significant challenge that the richness of image content can significantly affect autofocus performance. When the image content is sparse, previous autofocus methods, whether traditional climbing-hill or learning-based, tend to fail. To tackle this, we propose a content-importance-based solution, named SparseFocus, featuring a novel two-stage pipeline. The first stage measures the importance of regions within the image, while the second stage calculates the defocus distance from selected important regions. To validate our approach and benefit the research community, we collect a large-scale dataset comprising millions of labelled defocused images, encompassing both dense, sparse and extremely sparse scenarios. Experimental results show that SparseFocus surpasses existing methods, effectively handling all levels of content sparsity. Moreover, we integrate SparseFocus into our Whole Slide Imaging (WSI) system that performs well in real-world applications. The code and dataset will be made available upon the publication of this paper.
Logarithmic Regret of Exploration in Average Reward Markov Decision Processes
Venue: arXiv cs.LG 2025
Year: 2025

In average reward Markov decision processes, state-of-the-art algorithms for regret minimization follow a well-established framework: They are model-based, optimistic and episodic. First, they maintain a confidence region from which optimistic policies are computed using a well-known subroutine called Extended Value Iteration (EVI). Second, these policies are used over time windows called episodes, each ended by the Doubling Trick (DT) rule or a variant thereof. In this work, without modifying EVI, we show that there is a significant advantage in replacing (DT) by another simple rule, that we call the Vanishing Multiplicative (VM) rule. When managing episodes with (VM), the algorithm's regret is, both in theory and in practice, as good if not better than with (DT), while the one-shot behavior is greatly improved. More specifically, the management of bad episodes (when sub-optimal policies are being used) is much better under (VM) than (DT) by making the regret of exploration logarithmic rather than linear. These results are made possible by a new in-depth understanding of the contrasting behaviors of confidence regions during good and bad episodes.
EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices
Venue: arXiv cs.SE 2025
Year: 2025

The widespread adoption of machine learning on edge devices, such as mobile phones, laptops, IoT devices, etc., has enabled real-time AI applications in resource-constrained environments. Existing solutions for managing computational resources often focus narrowly on accuracy or energy efficiency, failing to adapt dynamically to varying workloads. Furthermore, the existing system lack robust mechanisms to adaptively balance CPU utilization, leading to inefficiencies in resource-constrained scenarios like real-time traffic monitoring. To address these limitations, we propose a self-adaptive approach that optimizes CPU utilization and resource management on edge devices. Our approach, EdgeMLBalancer balances between models through dynamic switching, guided by real-time CPU usage monitoring across processor cores. Tested on real-time traffic data, the approach adapts object detection models based on CPU usage, ensuring efficient resource utilization. The approach leverages epsilon-greedy strategy which promotes fairness and prevents resource starvation, maintaining system robustness. The results of our evaluation demonstrate significant improvements by balancing computational efficiency and accuracy, highlighting the approach's ability to adapt seamlessly to varying workloads. This work lays the groundwork for further advancements in self-adaptation for resource-constrained environments.
A Resilient and Energy-Efficient Smart Metering Infrastructure Utilizing a Self-Organizing UAV Swarm
Venue: arXiv cs.NI 2025
Year: 2025

The smart metering infrastructure may become one of the key elements in efficiently managing energy in smart cities. At the same time, traditional measurement record collection is performed by manual methods, which raises cost, safety, and accuracy issues. This paper proposes an innovative SMI architecture based on an unmanned aerial vehicle swarm organizing itself for the autonomous data collection in smart metering infrastructure with scalability and cost-effectiveness while minimizing risks. We design an architecture-based comprehensive system with various phases of operation, communication protocols, and robust failure-handling mechanisms to ensure reliable operations. We further perform extensive simulations in maintenance of precise formations during flight, efficient data collection from smart meters, and adaptation to various failure scenarios. Importantly, we analyze the energy consumption of the proposed system in both drone flight operations and network communication. We now propose a battery sizing strategy and provide an estimate of the operational lifetime of the swarm, underlining the feasibility and practicality of our approach. Our results show that UAV swarms have great potential to revolutionize smart metering and to bring a further brick to greener and more resilient smart cities.
Approximation Algorithms for Optimal Hopsets
Venue: arXiv cs.DS 2025
Year: 2025

For a given graph $G$, a "hopset" $H$ with hopbound $β$ and stretch $α$ is a set of edges such that between every pair of vertices $u$ and $v$, there is a path with at most $β$ hops in $G \cup H$ that approximates the distance between $u$ and $v$ up to a multiplicative stretch of $α$. Hopsets have found a wide range of applications for distance-based problems in various computational models since the 90s. More recently, there has been significant interest in understanding these fundamental objects from an existential and structural perspective. But all of this work takes a worst-case (or existential) point of view: How many edges do we need to add to satisfy a given hopbound and stretch requirement for any input graph? We initiate the study of the natural optimization variant of this problem: given a specific graph instance, what is the minimum number of edges that satisfy the hopbound and stretch requirements? We give approximation algorithms for a generalized hopset problem which, when combined with known existential bounds, lead to different approximation guarantees for various regimes depending on hopbound, stretch, and directed vs. undirected inputs. We complement our upper bounds with a lower bound that implies Label Cover hardness for directed hopsets and shortcut sets with hopbound at least $3$.
Decentralizing Trust: Consortium Blockchains and Hyperledger Fabric Explained
Venue: arXiv cs.DC 2025
Year: 2025

Trust models are essential components of networks of any nature, as they refer to confidence frameworks to evaluate and verify if their participants act reliably and fairly. They are necessary to any social, organizational, or computer network model to ensure truthful interactions, data integrity, and overall system resilience. Trust models can be centralized or distributed, each providing a good fair of benefits and challenges. Blockchain is a special case of distributed trust models that utilize advanced cryptographic techniques and decentralized consensus mechanisms to enforce confidence among participants within a network. In this piece, we provide an overview of blockchain networks from the trust model perspective, with a special focus on the Hyperledger Fabric framework, a widespread blockchain implementation with a consortium architecture. We explore Fabric in detail, including its trust model, components, overall architecture, and a general implementation blueprint for the platform. We intend to offer readers with technical backgrounds but not necessarily experts in the blockchain field a friendly review of these topics to spark their curiosity to continue expanding their knowledge on these increasingly popular technologies.